Nonlinear predictive control based on multivariable neural Wiener models

Abstract

This paper describes a nonlinear Model Predictive Control (MPC) scheme in which a neural Wiener model of a multivariable process is used. The model consists of a linear dynamic part in series with a steady-state nonlinear part represented by neural networks. A linear approximation of the model is calculated on-line and used for prediction. Thanks to it, the control policy is calculated from a quadratic programming problem. Good control accuracy and computational efficiency of the discussed algorithm are shown in the control system of a chemical reactor for which the classical MPC strategy based on a linear model is unstable. © 2011 Springer-Verlag.